College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
Neural Netw. 2019 May;113:11-19. doi: 10.1016/j.neunet.2019.01.003. Epub 2019 Feb 1.
In this paper, a new prediction and identification method based on adaptive echo state network (AESN) is proposed to identify a class of discrete-time dynamic nonlinear systems (DDNS). Firstly, according to the characteristics of input signals, the reservoir state update equation of AESN can be adaptively adjusted. In order to guarantee the echo state property of AESN, a sufficient condition for echo state property is given. Secondly, the reservoir parameters of AESN are optimized to improve the identification and prediction performance of AESN. Thirdly, an improved online output weights learning method based on historical reservoir state and output error is given. Finally, the effectiveness of the proposed method is verified by simulation examples.
本文提出了一种基于自适应回声状态网络(AESN)的新的预测和识别方法，用于识别一类离散时间动态非线性系统(DDNS)。首先，根据输入信号的特点，自适应调整 AESN 的储层状态更新方程。为了保证 AESN 的回声状态特性，给出了回声状态特性的充分条件。其次，优化 AESN 的储层参数，提高 AESN 的识别和预测性能。第三，提出了一种基于历史储层状态和输出误差的改进在线输出权值学习方法。最后，通过仿真示例验证了所提方法的有效性。
